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EndoCoT-Data

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Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 91, in _split_generators
                  pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 193, in _generate_tables
                  examples = [ujson_loads(line) for line in batch.splitlines()]
                              ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 20, in ujson_loads
                  return pd.io.json.ujson_loads(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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EndoCoT




Teaser

EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models

This repository contains the training data for EndoCoT, a novel framework that activates the reasoning potential of Multimodal Large Language Models (MLLMs) within diffusion frameworks through an iterative thought guidance module.

🌟 Highlights

  • EndoCoT is a reasoning paradigm for diffusion models that enables step-by-step inference.
  • It outperforms conventional training methods on complex tasks like Maze, TSP, VSP, and Sudoku.
  • Provides transparent, intermediate reasoning trajectories.

⚡ Quick Start

Setup environment 

git clone https://github.com/InternLM/EndoCoT
cd EndoCoT
conda create -n EndoCoT python=3.10
conda activate EndoCot
pip install -r requirements.txt

Sample Usage (Inference)

To test a single case using the codebase:

cd test
python test.py \
    --task Maze \
    --model_root /path/to/merged_ckpts \
    --lora_path /path/to/your_lora_weight.safetensors \
    --input_image ./data/sudoku_sample.png \
    --output_dir ./outputs/sudoku_results

Training

  1. Download the datasets & metadata.csv and ensure they are placed in the same directory.
  2. Run the training scripts:
cd DiffSynth-Studio
bash add/Maze/stage1.sh
python change_ckpt_prefix.py --src /path/to/the/Maze/save/dir/Maze_stage1	
bash add/Maze/stage2.sh
python change_ckpt_prefix.py --src /path/to/the/Maze/save/dir/Maze_stage2

📰 News

📖 Citation 

@article{dai2026endocot,
  title={EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models},
  author={Dai, Xuanlang and Zhou, Yujie and Xing, Long and Bu, Jiazi and Wei, Xilin and Liu, Yuhong and Zhang, Beichen and Chen, Kai and Zang, Yuhang},
  journal={arXiv preprint arXiv:2603.12252},
  year={2026}
}

⚖️ License

The code in the associated repository is licensed under the MIT License. The dataset is licensed under the CC BY-NC 4.0 License.
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